One4Many-StablePacker: An Efficient Deep Reinforcement Learning Framework for the 3D Bin Packing Problem

2025-10-11

AI Summary

Existing learning-based approaches for the 3D bin packing problem (3D-BPP) in logistics neglect stability constraints and suffer from poor generalization across diverse container sizes. Method: We propose a deep reinforcement learning framework enabling unified training over multiple bin dimensions. It introduces a weighted reward function and a height-difference metric to enhance loading plan flatness; incorporates clipped policy gradients and a customized policy drift mechanism to mitigate policy entropy collapse and improve exploration of critical actions; and jointly enforces load-bearing capacity and geometric stability constraints for end-to-end deployable solutions. Contribution/Results: The framework achieves strong generalization across varying bin sizes, significantly outperforms state-of-the-art baselines in packing utilization, and consistently satisfies practical stability requirements—demonstrating both efficacy and applicability in real-world logistics scenarios.

Technology Category

• Machine Learning: Reinforcement Learning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The three-dimensional bin packing problem (3D-BPP) is widely applied in logistics and warehousing. Existing learning-based approaches often neglect practical stability-related constraints and exhibit limitations in generalizing across diverse bin dimensions. To address these limitations, we propose a novel deep reinforcement learning framework, One4Many-StablePacker (O4M-SP). The primary advantage of O4M-SP is its ability to handle various bin dimensions in a single training process while incorporating support and weight constraints common in practice. Our training method introduces two innovative mechanisms. First, it employs a weighted reward function that integrates loading rate and a new height difference metric for packing layouts, promoting improved bin utilization through flatter packing configurations. Second, it combines clipped policy gradient optimization with a tailored policy drifting method to mitigate policy entropy collapse, encouraging exploration at critical decision nodes during packing to avoid suboptimal solutions. Extensive experiments demonstrate that O4M-SP generalizes successfully across diverse bin dimensions and significantly outperforms baseline methods. Furthermore, O4M-SP exhibits strong practical applicability by effectively addressing packing scenarios with stability constraints.

Problem

• Addressing 3D bin packing with stability and weight constraints
• Generalizing across diverse bin dimensions in single training
• Improving bin utilization through flatter packing configurations

Innovation

• Handles various bin dimensions in single training
• Uses weighted reward with height difference metric
• Combines clipped gradient with policy drifting method